MixPT: Point Transformer with Interleaved Window and Global Attention
Point cloud transformers aim to capture detailed local geometry and relationships across a scene at a manageable computational cost. Two common approaches are window attention and global attention. They differ in how they enable scene-wide interaction, which refers to information exchange among tokens across a scene. Window attention reduces computational cost by restricting self-attention to groups of tokens called windows; in serialized designs, each window contains a fixed number of consecutive tokens from a spatially ordered sequence. Because each block only directly connects tokens within the same window, information exchange across windows relies on changes in window membership acro...
| Authors | Minkyun Seo, Hyungtae Lim, Jaesik Park |
|---|---|
| Primary category | cs.CV |
| Published | 22 October 2026 |
| Last updated at source | 22 October 2026 |
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